class_docstr: Wrap a NeuronSimulator and calibrate it to map from the predicted activation space to the
  actual neuron activation space.
docstr: Determine parameters to map from the predicted activation space to the real neuron
  activation space, based on a calibration set.
  
  Take numpy arrays of all true activations and all uncalibrated activations on the
  calibration set over all sequences.
docstr_tok: ["Determine","parameters","to","map","from","the","predicted","activation","space","to","the","real","neuron","activation","space",",","based","on","a","calibration","set",".","Take","numpy","arrays","of","all","true","activations","and","all","uncalibrated","activations","on","the","calibration","set","over","all","sequences","."]
code: def _calibrate_from_flattened_activations(
      self,
      true_activations: np.ndarray,
      uncalibrated_activations: np.ndarray,
  ) -> None:
      """
      Determine parameters to map from the predicted activation space to the real neuron
      activation space, based on a calibration set.
  
      Take numpy arrays of all true activations and all uncalibrated activations on the
      calibration set over all sequences.
      """
  
imports: from __future__ import annotations
  import asyncio
  from abc import abstractmethod
  from typing import Optional, Sequence
  import numpy
  from neuron_explainer.activations.activations import ActivationRecord
  from neuron_explainer.explanations.explanations import ActivationScale
  from neuron_explainer.explanations.simulator import NeuronSimulator, SequenceSimulation
  from sklearn import linear_model
label_desc: Use image node_id 3 for calling the CalibratedNeuronSimulator obj's underlying member method code with example usage: obj._calibrate_from_flattened_activations(true_activations, uncalibrated_activations) without return types